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All Journal SCHOULID: Indonesian Journal of School Counseling
Nefrima Sartika Putri
SMP Negeri 26 Padang
Humanities Education Environmental Science Social Sciences Other

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Human-AI collaborative problem solving in middle school mathematics: an orchestration and learning review Nefrima Sartika Putri; Rudyanto Rudyanto
SCHOULID: Indonesian Journal of School Counseling Vol. 11 No. 1 (2026): SCHOULID: Indonesian Journal of School Counseling
Publisher : Indonesian Counselor Association (IKI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23916/086845011

Abstract

Artificial intelligence, particularly generative AI, is increasingly positioned as an active participant in learning rather than a passive digital tool. This systematic literature review synthesizes evidence on human-AI collaborative problem solving in mathematics, with particular attention to AI roles, collaboration patterns, learning outcomes, and pedagogical orchestration. The review followed PRISMA 2020. A Scopus title-abstract-keyword search produced 369 source records; after 10 duplicates were removed, 359 records were screened, 125 full-text reports were assessed, and 10 studies were retained for qualitative synthesis. Because the original screening archive did not preserve reviewer-level decision logs, inter-rater agreement coefficients could not be retrospectively computed without reconstruction; this limitation and a prescribed dual-reviewer rerun protocol are reported explicitly. The synthesis identified four recurring AI roles: adaptive scaffold/tutor, cognitive or visualization tool, co-creative partner, and orchestration platform. Productive collaboration was most consistently associated with socially structured routines, shared agency, reflective activity, and explicit teacher orchestration, whereas unstructured use increased the risk of cognitive off-loading and over-delegation. Learning gains were reported for problem solving, conceptual understanding, motivation, self-efficacy, and related outcomes, but causal evidence remains limited by heterogeneous designs, levels, and measures. The review therefore treats orchestration—not model capability alone—as the principal design mechanism and recommends preregistered, longitudinal, middle-school studies with process-level measures, independent dual screening, and multi-database searching.
Transparency and trust in AI-driven mathematics learning: a systematic review of explainability frameworks Nefrima Sartika Putri; Rudyanto Rudyanto
SCHOULID: Indonesian Journal of School Counseling Vol. 10 No. 3 (2025): SCHOULID: Indonesian Journal of School Counseling
Publisher : Indonesian Counselor Association (IKI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23916/086846011

Abstract

Explainable artificial intelligence (XAI) is increasingly used to support prediction, assessment, feedback, and instructional decisions in mathematics education, yet evidence about how explanation affects transparency, trust, and learning remains fragmented. This systematic literature review synthesized evidence on explainability in mathematics and closely related STEM education, with particular attention to the under-studied middle-school learner. The review followed PRISMA 2020 and used a structured TITLE-ABS-KEY search of Scopus. The search returned 646 records; 550 were excluded during title/abstract screening, 96 reports were assessed at full text, and 10 studies were retained for qualitative thematic synthesis. Three themes emerged: interpretable prediction of performance and at-risk learners; transparency, trust, and adoption; and explainable scaffolding and feedback. The evidence base is dominated by 2025 studies and by educator- or institution-facing prediction, while direct learner-facing explanation and experimentally tested effects on achievement remain scarce. SHAP, feature-importance methods, neuro-fuzzy models, and explanatory feedback systems improve access to model reasoning, but explanation quality alone has not been shown to cause higher achievement. The review therefore distinguishes technical interpretability from pedagogical usefulness and identifies validated measurement needs for teacher decision quality, student self-regulation, calibrated AI trust, and equitable treatment. Because the archived screening and appraisal logs were not preserved, retrospective inter-rater coefficients and aggregate quality scores could not be reconstructed without inventing data; this limitation is reported explicitly. Future studies should use dual-reviewer screening, design-appropriate critical appraisal, classroom-embedded experiments, and age-appropriate explanations.
Co-Authors - Rudyanto